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MLG
2007
Springer

Transductive Rademacher Complexities for Learning Over a Graph

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Transductive Rademacher Complexities for Learning Over a Graph
Recent investigations [12, 2, 8, 5, 6] and [11, 9] indicate the use of a probabilistic (’learning’) perspective of tasks defined on a single graph, as opposed to the traditional algorithmical (’computational’) point of view. This note discusses the use of Rademacher complexities in this setting, and illustrates the use of Kruskal’s algorithm for transductive inference based on a nearest neighbor rule.
Kristiaan Pelckmans, Johan A. K. Suykens
Added 08 Jun 2010
Updated 08 Jun 2010
Type Conference
Year 2007
Where MLG
Authors Kristiaan Pelckmans, Johan A. K. Suykens
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